참고문헌
- Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., and Lloyd, S. (2017), Quantum machine learning. Nature. Vol. 549, pp. 195-202. https://doi.org/10.1038/nature23474
- Cho, D.Y. and Kim, E.M. (2010), Extraction of spatial information of tree using LIDAR data in urban area. The Journal of Korean Society for Geospatial Information Science, Vol. 18, No. 4, pp. 11-20. (in Korean with English abstract)
- Choi, S.P., Yang, I.T., and Cong, J.H. (2002), Evaluation of horizontal position accuracy of facilities in digital map. The Journal of Korean Society for Geospatial Information Science, Vol. 10, No. 4, pp. 95-103. (in Korean with English abstract)
- Daneshtalab, S. and Rastiveis, H. (2017), Decision level fusion of orthophoto and LIDAR data using confusion matrix information for land cover classification, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017 Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7-10 October, Tehran, Iran, pp. 59-64.
- Feng, Q., Liu, J., and Gong, J. (2015), UAV remote sensing for urban vegetation mapping using random forest and texture. Remote Sensing, Vol. 7, No. 1, pp. 1074-1094. https://doi.org/10.3390/rs70101074
- Han, S.H. (2016), Introduction to Photogrammetry and Remote Sensing, Goomibook, Seoul.
- Hong, I.Y. (2017), Land use classification using LBSN(Location-Based Social Network) data and machine learning technique, Journal of the Korean Cartographic Association, Vol. 17, No. 3, pp. 59-67. (in Korean with English abstract) https://doi.org/10.16879/jkca.2017.17.3.059
- Hong, S.P. and Kim, E.M. (2018), Classification of 3D road objects using machine learning. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 535-544. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.6.535
- Jeong, D.H. and Jeong, W.T. (2019), Prediction of rolling noise based on machine learning technique using rail surface roughness data, Journal of the Korean Society for Railway, Vol. 22. No. 3, pp. 209-217. (in Korean with English abstract) https://doi.org/10.7782/jksr.2019.22.3.209
- Jo, W.H., Lim, Y.H., and Park, K.H. (2019), Deep learning based land cover classification using convolutional neural network: a case study of Korea. Journal of the Korean Geographical Society, Vol. 54, No. 1, pp. 1-16. (in Korean with English abstract)
- Jonsson, Sigurbjorn. (2019), RGB and multispectral UAV image classification of agricultural fields using a machine learning algorithm, Master's thesis, Lund University, Lund, Sweden, 88p.
- Jung, I.G., Sung, J.H., Lee, C.K., Kim, S.C., and Lee, Y.B. (2004), The prediction of spatial variability for soil information in Paddy field, Journal of Biosystems Engineering, Vol. 29, No. 1, pp. 65-70. (in Korean with English abstract) https://doi.org/10.5307/JBE.2004.29.1.065
- Kim, G.M. and Choi, J.W. (2018), Detection of cropland in reservoir area by using supervised classification of UAV imagery based on GLCM. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 433-442. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.6.433
- Kim, E.M., Cho, H.S., and Park, J.H. (2017), Analysis of applicability of orthophoto using 3D mesh on aerial image with large file size, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 3, pp. 155-166. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2017.35.3.155
- Kim, J.K., Lee, K.B., and Hong, S.G. (2017), ECG-based biometric authentication using random forest. Journal of the Institute of Electronics and Information Engineers, Vol. 54, No. 6, pp. 100-105. (in Korean with English abstract) https://doi.org/10.5573/ieie.2017.54.6.100
- Kwon, S.K., Lee, Y.S., Kim, D.S., and Jung, H.S. (2019), Classification of forest vertical structure using machine learning analysis, Korean Journal of Remote Sensing, Vol. 35, No. 2, pp. 229-239. (in Korean with English abstract) https://doi.org/10.7780/KJRS.2019.35.2.3
- Lee, G.W. and Yom, J.H. (2018), Design and implementation of web-based automatic preprocessing system of remote sensing imagery for machine learning modeling. The Journal of Korean Society for Geospatial Information Science, Vol. 26, No. 1, pp. 61-67. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2018.26.1.061
- Liu, D. and Xia, F. (2010), Assessing object-based classification: advantages and limitations, Remote Sensing, Vol. 1, No. 4, pp. 187-194.
- Liu, P. (2015), A survey of remote-sensing big data. Frontiers in Environmental Science, Vol. 3, No. 45, pp. 1-6.
- Park, G.M. and Bae, Y.C. (2019), Performance comparison of machine learning in the various kind of prediction. The Journal of the Korea Institute of Electronic Communication Sciences, Vol. 14, No. 1, pp. 169-178. (in Korean with English abstract) https://doi.org/10.13067/JKIECS.2019.14.1.169
- Park, H.K. and Lee, D.K. (2019), Disaster prediction and policy simulation for evaluating mitigation effects using machine learning and system dynamics: case study of seasonal drought in gyeonggi province. Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 1, pp. 45-53. (in Korean with English abstract) https://doi.org/10.9798/kosham.2019.19.1.45
- Park, S., Kim, K.J., Lee, J.S., and Lee, S.R. (2011), Red tide prediction using neural network and SVM, The Institute of Electronics Engineers of Korea-Signal Processing, Vol. 48. No. 5, pp. 39-45. (in Korean with English abstract)
- Schonberger, J.L. and Frahm, J.M. (2016), Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 27-30 June, Las Vegas, USA, pp. 4104-4113.
- Schopfer, E., Lang, S., and Strobl, J. (2010), Segmentation and object-based image analysis. Remote sensing of urban and suburban areas, Vol. 10, pp. 181-192. https://doi.org/10.1007/978-1-4020-4385-7_10
- Wang, C. and Li, Z. (2016), Weed recognition using SVM model with fusion height and monocular image features. Transactions of the Chinese Society of Agricultural Engineering, Vol. 32, No. 15, pp. 181-192.
- Wikipedia. (2019), Random forest, Wikimedia Foundation, Inc., URL: https://en.wikipedia.org/wiki/Random_forest(last date accessed: 26 June 2019).
- Yamamoto, K., Guo, W., Yoshioka, Y., and Ninomiya, S. (2014). On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors, Vol. 14, No. 7, pp. 12191-12206. https://doi.org/10.3390/s140712191
- Yu, B.H., P, H.C., and Lee, S.M. (2019), Improvement of randomforest OBIA algorithm for tree anomaly detection in UAV imagery: Focused on the Birobong-Peak Area of Sobaeksan National Park. The Korean Society of Environment and Ecology, 26 April, Wonju, Korea, pp. 54-54.
- Yun, T.G. and Yi, G.S. (2008), Application of random forest algorithm for the decision support system of medical diagnosis with the selection of significant. The transactions of The Korean Institute of Electrical Engineers, Vol. 57, No. 6, pp. 1058-1062. (in Korean with English abstract)
- Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016), An easy-to-use airborne LiDAR data filtering method based on cloth simulation, Remote Sensing, Vol. 8, No. 6, pp. 501. https://doi.org/10.3390/rs8060501
피인용 문헌
- 포인트 클라우드에서 딥러닝을 이용한 객체 분류 및 변화 탐지 vol.50, pp.2, 2019, https://doi.org/10.22640/lxsiri.2020.50.2.37